At Netflix, we entertain and bring joy to people across the world through amazing stories. We think of the Netflix service less as a monolithic product and more as hundreds of millions of different products by serving uniquely personalized experiences to each of our 139+ Million members. Machine learning is at the center of our personalization. One of the teams powering this effort is the Personalization Infrastructure team, which is building a scalable Machine Learning platform to accelerate innovation for Netflix recommendations, content promotion, and search algorithms.
Our team’s main challenge is to scale machine learning at all stages of a project’s life cycle, including ad-hoc exploration and experimentation, preparing training data, model development, and robust production deployment. To enable continuous innovation, this must be done in heterogeneous language environments across several domains. We need to do this at the scale of hundreds of million global streaming members whose viewing habits, preferences, and contexts change over time. To meet these business and research needs, we innovate on the platform infrastructure for a fast and continuous cycle of learning, inference, and observation while maintaining high system reliability.
In this role, you will define and execute a strategy for exploring hyper parameters for machine learned models. You will build the systems and infrastructure needed to integrate and orchestrate in-house and third party optimizers in both offline and online contexts. You will need to have the appropriate end-user focus to define ergonomic APIs used by ML researchers. You may also occasionally partner with researchers for end-to-end modeling and data exploration for a particular member-facing A/B test.
To be successful in this role, you will need the ability to learn quickly, work cross functionally with several teams, and be a thought-leader who can influence within and around their team. This role will allow you to work on one of the premier recommender systems in the world, while working for a unique and pioneering company that is redefining how video content is consumed globally.
Strong bias towards action, great curiosity, and excellent communication skills
Experience designing end-user software with good API design sensibilities
Exposure to working with high-scale distributed systems as a generalist
BS/MS in Computer Science, Electrical Engineering or a related field
Experience with functional languages like Clojure/Lisp (a strong plus)
Exposure to modern experimentation and A/B Testing methodologies
Experience working with production data pipelines
Broad conceptual understanding of machine learning
Experience using Spark with Scala and Python and using Amazon AWS
Exposure to the Recommender Systems domain
To learn more about our domain and team, here are some relevant papers, talks and blog posts:
You can learn more about Netflix’s unique take on Freedom & Responsibility, that presents an opportunity to work with some of the best and the brightest, allowing you to make a difference to the business in a meaningful way.
Netflix is the world’s leading Internet television network with over 100 million members in over 190 countries enjoying more than 125 million hours of TV shows and movies per day, including original series, documentaries and feature films. Members can watch as much as they want, anytime, anywhere, on nearly any Internet-connected screen. Members can play, pause and resume watching, all without commercials or commitments.